Given an ordered sample of either exceedances or upper order statistics which is to be modeled using a GPD, this
function provides Falk's estimator of the shape parameter γ \in [-1,0]. Precisely,

for $H$ either the empirical or the distribution function based on the log–concave density estimator.
Note that for any k, \hat γ_{\rm{Falk}} : R^n \to (-∞, 0). If
\hat γ_{\rm{Falk}} \not \in [-1,0), then it is likely that the log-concavity assumption is violated.